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  5. Google Cloud Data Fusion vs Trifacta

Google Cloud Data Fusion vs Trifacta

OverviewDecisionsComparisonAlternatives

Overview

Trifacta
Trifacta
Stacks19
Followers41
Votes0
Google Cloud Data Fusion
Google Cloud Data Fusion
Stacks25
Followers156
Votes1

Google Cloud Data Fusion vs Trifacta: What are the differences?

Introduction

In this article, we will compare Google Cloud Data Fusion and Trifacta, two popular data integration and transformation tools. We will explore their key differences to help you understand which one may be best suited for your specific needs.

  1. Integration with Cloud Platforms: Google Cloud Data Fusion is specifically designed to seamlessly integrate with Google Cloud Platform (GCP) services. It provides native connectivity to various GCP data sources and can leverage GCP's capabilities, such as BigQuery, Cloud Storage, and Pub/Sub for data processing. On the other hand, Trifacta offers broader integration capabilities, allowing users to connect with various cloud platforms like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud, as well as on-premises and hybrid environments.

  2. User Interface and Ease of Use: Google Cloud Data Fusion provides a visual, code-free development environment with a no-code/low-code approach. It offers a drag-and-drop interface to design and build data pipelines and transformations, making it more accessible to business users and data analysts. Trifacta, on the other hand, adapts a user-centric approach with an intuitive interface and AI-powered data wrangling capabilities. Its data wrangling functionalities allow users to prepare data for analysis without the need for extensive coding knowledge.

  3. Data Transformation Capabilities: Google Cloud Data Fusion offers a wide range of pre-built connectors and transformations, enabling users to integrate and transform data from multiple sources easily. It provides a marketplace with pre-built pipeline templates and transformations for common use cases. Trifacta focuses more on interactive data wrangling, providing advanced features like data profiling, data cleansing, and data quality assessment. It empowers users to visually explore, transform, and cleanse their data for further analysis.

  4. Scalability and Performance: Both Google Cloud Data Fusion and Trifacta are designed to scale horizontally, allowing users to handle large volumes of data. However, Google Cloud Data Fusion leverages Google Cloud's infrastructure, benefiting from its scalability and performance capabilities. It can dynamically scale resources based on workload demands, ensuring efficient data processing. Trifacta also offers scalable processing but may have some limitations in terms of performance when dealing with complex transformations on large datasets.

  5. Governance and Security: Google Cloud Data Fusion provides robust security measures, including encryption at rest and in transit, access controls, and auditing capabilities. It ensures data privacy and complies with various data protection regulations. Trifacta also emphasizes data security and governance, offering features like role-based access control, data lineage tracking, and data masking. Both tools have built-in security features, but Google Cloud Data Fusion benefits from Google Cloud's extensive security infrastructure.

  6. Ecosystem and Extensibility: Google Cloud Data Fusion, being a Google Cloud service, can leverage the broader GCP ecosystem and integrate with other GCP services seamlessly. It can take advantage of GCP's machine learning services, serverless computing, and other analytics tools. Trifacta, being platform-agnostic, allows integration with a wide range of tools and ecosystems. It has connectors to popular data warehouses, BI tools, and data science platforms, offering flexibility to work within existing environments.

Summary

In summary, Google Cloud Data Fusion is ideal for users looking for native integration with Google Cloud Platform and a visual, code-free development environment. It offers pre-built connectors and transformations for rapid data integration and processing. Trifacta, on the other hand, provides broader integration capabilities across different cloud platforms, including AWS, Azure, and Google Cloud. It focuses more on interactive data wrangling and advanced data transformation capabilities. Choose Google Cloud Data Fusion for seamless integration with GCP and ease of use, while Trifacta offers more versatility and extensive data wrangling capabilities.

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Advice on Trifacta, Google Cloud Data Fusion

Sarah
Sarah

Jun 25, 2020

Needs adviceonOpenRefineOpenRefine

I'm looking for an open-source/free/cheap tool to clean messy data coming from various travel APIs. We use many different APIs and save the info in our DB. However, many duplicates cannot be easily recognized as such.

We would either write an algorithm or use smart technology/tools with ML to help with product management.

While there are many things to be considered, this is one feature that it should have:

"To avoid confusion, we need to merge the suppliers & products accordingly. Products and suppliers must be able to be merged and assigned separately.

Reason: It may happen that one supplier offers different products. E.g., 1 tour operator offers 3 products via 1 API, but only 1 product with 3 (or a different amount of) variations via a different API. Also, the commission may differ for products, which we need to consider. Very often, products that are live (are bookable in real-time) on via 1 API, but are not live on the other. E.g., Supplier product 1 & 2 of API1 are live, product 3 not. For the same supplier, API2 provides live availability for products 1, 2, and 3.

Summing up, when merging the suppliers (tour operators) we need to consider:

  • Are the products the same for all APIs?
  • Which booking system API gives a better commission? Note: Some APIs charge us 1-5% depending on the monthly sale, which needs to be considered
  • Which booking system provides live availability
  • Is it the same supplier, or is the name only similar?

Most of the time, the supplier names differ even if they are the same (e.g., API1 often names them XX Pty Ltd, while API2 leaves "Pty Ltd" out). Additionally, the product title, description, etc. differ.

We need to write logic and create an algorithm to find the duplicates & to merge, assign, or (de)activate the respective supplier or product. My previous developer started a module to merge the suppliers, which does not seem to work correctly. Also, it is way too time taking considering the high amount of products that we have.

I would recommend merging, assigning etc. products and suppliers only if our algorithm says it's 90- 100% the matching supplier/product. Otherwise, admins need to be able to check & modify this. E.g. everything with a lower possibility of matching will be matched automatically, but can be undone or modified.

The next time the cron job runs, this needs to be considered to avoid recreating duplicates & creating a mess."

I am not sure in what way OpenRefine can help to achieve this and what ML tool can be connected to learn from the decisions the product management team makes. Maybe you have an idea of how other travel portals deal with messy data, duplicates, etc.?

I'm looking for the cheapest solution for a start-up, but it should do the work properly.

19.2k views19.2k
Comments

Detailed Comparison

Trifacta
Trifacta
Google Cloud Data Fusion
Google Cloud Data Fusion

It is an Intelligent Platform that Interoperates with Your Data Investments. It sits between the data storage and processing environments and the visualization, statistical or machine learning tools used downstream

A fully managed, cloud-native data integration service that helps users efficiently build and manage ETL/ELT data pipelines. With a graphical interface and a broad open-source library of preconfigured connectors and transformations, and more.

Interactive Exploration; Automated visual representations of data based upon its content in the most compelling visual profile; Predictive Transformation; Intelligent Execution; Collaborative Data Governance.
Code-free self-service; Collaborative data engineering; GCP-native; Enterprise-grade security; Integration metadata and lineage; Seamless operations; Comprehensive integration toolkit; Hybrid enablement
Statistics
Stacks
19
Stacks
25
Followers
41
Followers
156
Votes
0
Votes
1
Pros & Cons
No community feedback yet
Pros
  • 1
    Lower total cost of pipeline ownership
Integrations
Microsoft Azure
Microsoft Azure
Google Cloud Storage
Google Cloud Storage
Snowflake
Snowflake
AWS Data Pipeline
AWS Data Pipeline
Tableau
Tableau
Google Cloud Storage
Google Cloud Storage
Google BigQuery
Google BigQuery

What are some alternatives to Trifacta, Google Cloud Data Fusion?

Apache Spark

Apache Spark

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

Presto

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

Apache Flink

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

Apache Kylin

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Splunk

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Apache Impala

Apache Impala

Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.

Vertica

Vertica

It provides a best-in-class, unified analytics platform that will forever be independent from underlying infrastructure.

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